Return on Equity, Return on Asset and Stock Returns Behavior of Quoted Manufacturing Firms in Nigeria

materials for additional research in this area. This report can be used by regulatory agencies such as the Securities and Exchanges Commission , the Central Bank of Nigeria , as well as the Manufacturers Association of Nigeria to enhance the framework for regulating Nigerian manufacturing firms. The study's findings will also help regulators and policymakers enact new rules and guidelines pertaining to working capital administration in manufacturing companies. JAFM JAFM 2.0 Review of Related Literature 2.1 Conceptual Review The conceptual issues are discussed in this section of the study. 2.1.1 Overview of Accounting Income According to Meyer (1984) accounting plays a significant role within the concept of generating and communicating wealth of companies. Financial statements still remain the most important source of externally feasible information on companies. 2.1.2 Proxies of Accounting Income The commonly used proxies for accounting income are explained in this section 2.1.3 Returns on Asset This ratio displays profitability of a business in proportion to its total size. How well management utilizes all of the assets owned by the business to create revenue is demonstrated by their return on assets. The return rises when management makes better use of its asset base. The ROA ratio, which is typically given as a percentage, is computed by dividing the mean total assets by the net income. Since a firm's assets are only used to produce income and profits, this ratio shows investors and management how well a company can turn asset investments into earnings. The monetary value of the assets of a business is assessed by this ratio. Because it shows that the company is managing its wealth more effectively to produce higher levels of net income, investors prefer a larger ratio. Generally speaking, an elevated ROA ratio denotes an increasing trend in profits. It is calculable using the formula. Return on Assets: Model 2.1 Net Income Total Asset 2.1.4 Returns on Equity, or ROE The percentage of net income return to shareholders' equity is known as returns on equity . According to Fuhrmann (2017), return on equity measures a corporation’s profitability by revealing how much profit a company generates with the money shareholders have invested. Equity is the factor that counts for ROE, particularly shareholders' equity. Shareholders equity is the amount of money in accounting that is left over for shareholders when a company uses its reported assets to pay down its outstanding debts and it is determined by deducting creditors from resources on the financial status statement of the business. ROE is computed as follows: Net Income Total Shareholders′Equity 2.1.5 Conceptual Overview of Stock Returns Historical accounting data, particularly financial ratios, can be used to forecast stock returns. Basic components like profitability, solvency, liquidity, along with operational efficiency are helpful when developing profitable fundamental investing methods. Additionally, there is a positive link between high-performing companies and these cumulative basic indications (Sakr, 2022). Fundamental indicators such as operating efficiency, solvency, liquidity, and profitability metrics (ROA, CFO) are found to be important predictors of prospective earnings both immediately and later (Dynas and Hancock, 2022). Furthermore, the financial improvements could have a big influence on current market returns and aid in forecasting future profits. Model 2.2 JAFM JAFM 2.1.6 Stock Returns and Returns on Assets Return on Assets is a financial metric used to evaluate whether management has received a reasonable return from the assets under its control. This ratio is a useful measure if one wants to appraise how well the company has used its funds. According to Tandelilin (2020), Return on Assets is the company's ability to earn profits from the assets owned by the company. ROA is defined by Hanafi and Halim (2016) as the ability of the business to make money in relation to its assets. The collective opinions of investors about a company's success, both now and in the future, are reflected in its values. The company's value depends on the opportunity to grow; however, this opportunity depends on its ability to attract capital. According to Brigham and Houston (2020), the ratio of operating profitability to total assets is known as returns on assets , and it is used to calculate the value of total assets. The authors claim that ROA is a macroeconomic metric that shows the percentage of returns on resources of a business's profits that originate from its own internal assets. One of the profitability ratios is ROA. The greater the ROA percentage, the greater the profit that the business will make. 2.1.7 Stock Returns and Returns on Equity Return on equity , sometimes referred to as success of own investment, is a ratio to measure net profit after tax with own capital and generate net profit available to owners or investors, (Kasmir, (2012). ROE shows whether management was effective in optimizing the returns on the investment made by shareholders and emphasizes the return on income relative to the investment amount. The financial return is calculated via ROE earned from investments for shareholders by dividing the number of shares of common equity by the amount of net cash available to common shareholders. Kasmir (2012) claims that a greater own capital profitability ratio is preferable since it indicates how much capital is used to generate multiple profits, most notably net profit after taxes. Conversely, a lower own capital profitability ratio indicates that operational capital only generates a modest or nonexistent net profit after taxes. Returns on equity is a metric of profitability from the viewpoint of the investor rather than the company. Net profit after taxes is divided by own capital to calculate ROE. Given that the return on equity is a crucial metric of how well a company will use the invested capital to generate net income, it is taken into consideration in this study. The rate of returns on investment for shareholders is measured by returns on equity (Brigham and Houston, 2016). ROE is a measure of a company's capacity to generate profits from its capital. An alternative term for ROE is the percentage of the value of the net assets of the business or the profit received from owner's equity. As a result, ROE evaluates the business's capacity to generate profits for its owners. Shareholders carefully consider this ratio since they seek high ROE. 2.2 Theoretical Review The theory upon which this study anchored are discussed in this section of the study. 2.1.1 Free Cash Flow Theory The free cash flow theory, first presented in Michael Jensen in 1986, serves as the basis for this investigation. According to this theory, business free cash flows refer to the surplus cash flows that arise after netting off the necessary funds invested in projects that yield positive net present value returns. These projects are long-term investment projects whose expected present value cash inflows are more than their cash outflows (Jensen, 1986). FCF is an effective metric for assessing the firm's success since it is connected to changes in shareholders' wealth, which is linked to stock return (Maham et al., 2008). FCFs indicate the JAFM JAFM flow of cash from operations (the ability to meet its operational obligations), financial revenue flow (the ability of the organization to repay debts, sell and repurchase its stock), and leveraging cash flow (the facilitation of quicker enterprise growth through prudent the use of fixed assets), all of which increase investors' confidence in the company. According to Jensen (1986), these are a couple of the factors that lead many investors to believe that higher FCF indicates higher business value, which is reflected in a high return of stock. Conversely, the business's shareholders think that its free cash flow can be a criterion for generating value for them. This is because companies with high beneficial FCF are likely to use the extra money for new, profitable ventures that generate positive net present value , which eventually boosts stock returns. This theory is important to the study because the theory was founded on the idea that businesses give priority to the sources of funding for their investments. Theorists contend that investors and management have unequal access to information. 2.3 Review of Empirical studies Samoei and Tenai (2021) established the effect of operating cash flow ratio on stock return of firms listed in NSE. The study was informed by Free Cash Flow theory. Census survey was adapted to review financial statements for 29 listed non-financial firms at NSE that had consistent data for all the study variables. Secondary data was extracted for 12 years from 2007- 2019 with the aid of a data collection sheet. Explanatory research design which is panel in nature was followed by this study. Both descriptive and inferential statistics were used in data analysis. Panel data regression was used to make inferences and test research hypothesis. Fixed and Random effects methods were used to analyze the balanced panel data using STATA statistical package and Hausman test established that Random effect model was the most ideal method to analyze data in this study. The findings indicated that operating cash flow positively and significantly influenced the stock returns for firms listed at NSE. The study concludes that operating cash flow ratio information affects stock returns. Therefore, the study advocates for firms to increase their levels of operating cash flows through prudent utilization of cash resources since it enhances the stock returns. Olaniyan et al. (2022) explored the influence of operating cash flow ratio on firm performance in Nigeria. It specifically examined the influence of total operating expenses on return on asset, debt on equity, equity market value, total income growth rate, and net income growth rate. The study predicated on free cash flow theory, Trade off theory, and pecking order theory. Secondary data were used to carry out the facts of the situation, which were obtained through the annual financial report of the firm, which covered a period of thirty years spanning from 1990-2020. Data were analyzed using descriptive research design to test the level of co- integration among the variables. The study revealed that the variables used in this study are co- integrated in the long-run which led to the Vector Error Correction Model test, which revealed that all variables of operating cash flow ratio incorporated in the model have a positive effect on firm performance both in the short-run and long run. This means that in Nigeria, all independent variables produce the expected positive effect within the periods of study. It was concluded that adequate funds have been injected into the firm from time to time and have been well managed, which enhances the firm's to be more productive. Gunanta et al. (2020) examined the effect of the cash flow statement and Earning per Share on stock prices. The method used in this research is descriptive and verification method, where the data has been obtained from the Indonesia Stock Exchange Office - Bandung. This study also involves a literature search. The data is statistically analysed using SPSS 18.00. The statistical analyses conducted are the classical assumption test, multiple linear regression analysis, analysis of the coefficient of determination, t-test, and F test. This study suggests that JAFM JAFM EPS has significant influence on stock price in manufacturing companies listed on the Indonesia Stock Exchange for the period from 2008 to 2012. Duru et al. (2023), examined the effect of cash flow on performance of companies in Food and Beverages sub-sector of Nigeria. The study involved a survey of six companies of Food and Beverages companies quoted in the Nigerian Stock Exchange. Data were obtained from the Annual reports and accounts of the selected companies under study. The relevant data were analyzed using the multiple regression technique. The result of the study revealed that operating and financing cash flows have significant positive effect on corporate performance in the Food and Beverages Sector in Nigeria. It was also observed that investing cash flow has significant negative relationship with corporate performance. The researchers recommended that regulatory authority should encourage external auditors of these quoted Food and Beverages Companies to use cash flow ratios in evaluating the performance of a company before forming an independent opinion on the financial statement. Habib (2021) which evaluated present cash flow, consistent profitability and development potential on the stock returns in Australian stock market. The investigation, which relied on a multivariate regression technique, found a positive correlation between the growth prospects and free cash flow of a company and its market value. Adelegan (2023) conducted an empirical investigation of the connection between cash flow and monetary reforms in Nigeria. Over a longer testing period spanning 1984–1997, the researcher employed the ordinary least squares approach to analyze data on a sample of 63 listed enterprises in Nigeria. The empirical data shows a negative correlation between cash flow and corporate performance. Miar (2020) investigates the information content of cash flows financial ratios on the Tehran stock market, and finds that investors take no strong position either way. The data ranged from 1988 to 1994, and he ran it via the ordinary Least Squares method. Results showed a modest but statistically significant relationship between cash flow ratios and ratios from the income statement and the balance sheet, which in turn correlate with stock returns. Dastgir et al. (2011) investigated the relationship between cash flows ratio with stock return of companies. The research results indicated that there was not any significant relationship between operating cash flows ratio and stock returns except in 2003 and in the analysis of mixed data, it was concluded that there was no significant relationship between operating cash flow ratio and stock return using mixed data. In fact, at 5% error level, operating cash flows ratio did not provide necessary information content for determining stock returns. Also in the analysis of cross-sectional data, there was significant relationship between free cash flows and stock return. The calculated results for year 2002 and 2003 showed that there was not any relationship between variables. Ojimba et al. (2021) examined the effects of cash flow ratio on stock returns of consumer goods firms for a period of 2010-2019. The study was anchored on agency theory. Panel data were gotten from the Nigerian Stock Exchange and the data collected were analyzed using multiple regression analysis. The findings revealed Cash flows from operating activities has no significant effect on stock returns of consumer goods firms in Nigeria, cash flow from investing activities does not have significant effect on the stock returns of consumer goods firms in Nigeria, Cash flows from financing activities has no significant effect on stock returns of consumer goods firms in Nigeria, Free cash flow has positive and significant effect on stock returns of consumer goods firms in Nigeria. On the basis of the findings of the study, it was recommended among others that there is need for consumer goods industry to improve on their operating cash flow by making money available for this purpose for the general benefit of the economy. JAFM JAFM Okoye (2020) ascertained the effect of operating cash flow ratio on earnings management of Nigerian Banks. The study adopted Ex post facto research design. The study used sample of fifteen (15) Nigerian banks from 2010 to 2019. Data for the study was collected from annual reports and accounts of the banks. Regression analysis was used to test the hypothesis with the aid of E-view 9. 0. Based on this, the study revealed that operating activities are not statistically significant and have a negative effect on total accruals earnings of Nigerian banks. The study concludes that the importance of risk management activities is aimed at reducing future cash flow. Leo and Harlyn (2022) examined the effect of gross profit, operating profit and net profit on the prediction of future cash flows in telecommunications sub-sector companies listed on the Indonesian stock exchange. This research was conducted focused on the telecommunications sub-sector companies listed on the Indonesia Stock Exchange in 2014- 2019. The sampling method used is purposive sampling. Through this sampling method, 5 companies were obtained that could be used as samples with a research period of 6 years. Therefore, in this study, the number of this study was 30 units of analysis. This study uses descriptive statistical analysis, correlation coefficient analysis, coefficient of determination analysis, simple regression analysis, and multiple regression analysis in analyzing all data. Testing the data used in this study is the partial t-significance test, the classical assumption test, and the F simulation test. From the research conducted, the results show that the gross profit and operating profit variables partially have no significant effect on future cash flows while the net income variable shows that partially significant effect on cash flows in the future. The variables of gross profit, operating profit and net profit simultaneously show a significant effect on future cash flows. Gilbert and Ugochukwu (2023) evaluated how selected firms’ costs predict the directionality of operating profits of public listed consumer goods firms in Nigeria. Accordingly, the research intends to determine the effect of selling and distribution costs, cost of inventory and cost of labour on the operating profit ratio of the sampled firms. To achieve these objectives, the study adopts the ex-post facto research design. A total of 13 consumer goods firms was purposively sampled out of a population of 20 consumer goods firms that are listed on the floor of the Nigerian Exchange Group. Secondary data obtained from the 2011- 2020 annual reports of the selected firms were analysed using descriptive statistics, correlation analysis and ordinary least square regression technique at 5% level of significance. Findings made showed that cost of inventory is positive, but does not significantly drive the operating profit ratio of public listed consumer firms in Nigeria, cost of labour is positive and significantly drives the operating profit ratio of public listed consumer firms in Nigeria, while selling and distribution costs are negative, but do not significantly drive the operating profit ratio of sampled firms. Based on these findings, the research concludes that when an effective costing system or technique has been established in the firm, there are efficient allocation and utilization of resources, which lead to minimization of costs and maximization of profit. It was therefore recommended that managers of consumer goods companies should strengthen envisaged control procedures to eliminate waste in their selling and distribution costs. Gap in Empirical Literature Preliminary literature review indicates that empirical studies undertaken on the effect of accounting income on stock returns of selected Manufacturing Companies in Nigeria have not been exhaustive. More so, in very recent years, many studies explored the influence of accounting income on the organizational performance without examining the effect on the stock returns. Other studies tend to aggregate the components of accounting income, but failed to examine their distinct effect on the stock returns of Manufacturing Companies in Nigeria. Another potential gap is the scarcity of the study on the effect of accounting income on stock returns of Manufacturing Companies in Nigeria. Given a preliminary review, most studies in JAFM JAFM this area are mostly undertaken outside Nigeria. Also, there is scarcity of longitudinal studies that track the effect of accounting income on stock returns of Manufacturing Companies in Nigeria. Most studies offer cross-sectional analyses, which provide only a snapshot of the relationship. Longitudinal research could offer more robust insight into how accounting income evolve and their long-term effect on performance. 3.0 Methodology 3.1 Research Design Panel data was used for the study and this was based on ex-post facto research design. The panel data has the characteristics of time series and cross sectional as the data were collected from many companies in many years. The data collected already exist and the study made used of it in its original form. The study employed Ex-post facto, because it used data of past performance of the selected manufacturing organisation. 3.2 Population of the Study The research population comprises of thirty-five (35) manufacturing companies listed on the Nigerian Exchange Group as at December 2025. For the purpose of this study, nine (9) manufacturing companies were selected for the study. Table3.1: List of selected Manufacturing Companies in Nigeria Exchange Group i. Champion Brewery Plc ii. Guinness Nig. Plc iii. Nestle Nigeria Plc. iv. GlaxoSmithKline Consumer Nig. Plc. v. Lafarge Africa Plc vi. Livestock Feeds Plc. vii. Cadbury Nigeria Plc viii. Cutix Plc ix. Dangote Cement x. Lafarge Africa Plc xi. Meyer Plc xii. BUA Brewery xiii. Premier Paint Plc xiv. Berger Paints Plc xv. First Aluminum Nigeria Plc xvi. Unilever Nigeria Plc xvii. Golden Penny xviii. PZ Cussons xix. International Brewery Plc xx. Enamelware Plc xxi. BUA Cement xxii. May and Baker xxiii. Fidson Plc xxiv. Berger Plc Source: Researchers’ Compilation, (2026) JAFM JAFM ΔNI i,t Pri i,t-1 NI i,t Pri i,t-1 ΔNI i,t Pri i,t-1 NI i,t Pri i,t-1 3.3 Sampling Technique The study utilized simple random sampling technique and nine (9) manufacturing companies were selected from the purpose of the study. The goal of random sampling technique is to ensure that the sample is unbiased and representative of the population, allowing researchers to make inferences about the population based on the sample data. 3.4 Source and Nature of Data The study is a secondary research. Secondary data were obtained from audited annual reports published on the Nigerian Exchange Group and from Fact Books of the nine (9) manufacturing companies selected for the purpose of the study. 3.5 Measurement and Description of variables The variables in the regression model is expected as follows Apriori Expeactation Return on Equity Net Income Total share holder Equity Measures a company’s ability to generate profits from share holders’ equity. Positive Return on Asset Net Income Total Asset Evaluates how efficiently a company uses its assets to generate profits Positive Model Specification The model used was premised on the main objectives and anchored on the sub-objective. The following mathematical model were developed to analyse the effect of accounting income on stock returns of Manufacturing Companies in Nigeria using Earning Per Share , Return on Investment , Operational Cash Flow Ratio , Return on Equity , Return on Asset , and Operating Profit Margin as the explanatory variables to see how it affect Stock Returns (SR) which is the dependent variable. The study adapted Easton and Harris (1991) valuation model. This give the background to the work. It establishes that there is relationship between accounting income and stock returns. This prompted the researcher to adapt the model because it suit the researcher’s research variables. The Easton and Harris (1991) Valuation Model Easton and Harris’s (1991) valuation model expresses stock returns as a function of earnings levels and earnings changes, with both variables deflated by the stock price at the end of the previous year. In statistical notation, the model is as follows: Ret i, t = αo + α1 + α2 + Ɛ i,t Model 3.1 where Ret i,t is the stock return of firm i at time t, measured three months after the fiscal year end (Easton and Harris, 1991), the net income (NI) of firm i at time t, before taxes and extraordinary items (NIi,t) divided by the number of common shares outstanding and deflated by the market price at the end of the previous year (Pri t-1) and the change in the net income of firm i at time t, before taxes and extraordinary items (∆NI i,t) divided by the number of common shares outstanding and deflated by the market price at the end of the previous year (Pri,t-1). Last, ε i,t is an error term that follows a normal distribution with mean zero and standard deviation σε . The Easton and Harris model measures the information content of earnings levels and changes for stock returns and thus can be described as providing evidence on the differential relationship between earnings and prices. The model can be used to assess annual differences in the JAFM JAFM information content of the accounting variables between the pre- and post-IFRS periods. However, Easton (1999) provides some additional insights on the interpretation of the slope coefficients α1 and α2. Specifically, assuming that the clean surplus relation holds, he argues that slope coefficient α1 is a proxy for the statistical association between the stock price and the book values of equity per share. In addition, slope coefficient α2 measures the statistical association between stock prices and earnings per share. Econometrically the basic model is stated as; SRit = β0 + β4ROEit + β5ROAit + e Equation 3.1 This equation can be rewritten as putting the variables SR = f ( ROE. ROA, ) Model 3.2 Whereas: β0 = Intercept SR = Stock Return ROE = Return on Equity ROA = Return on Asset e = Error term t = Time dimension i = individual firm Method of Data Analysis The secondary data collected was analyzed using descriptive statistics, correlation analysis, regression and interaction analysis. Multiple regression analysis was used to evaluate the effect of the independent variables with the aid STATA. The result is to reveal the degree of influence and the level of significance. The collected research data were checked for any errors and omissions, coded, defined and then entered into STATA. This study used both descriptive and inferential statistics. A panel data were used to evaluate the hypotheses. First Order Econometric Tests The statistical criteria that was used to test the first-order hypotheses include: The economic apriori expectation evaluated the parameter in terms of their meeting the standard economic theory expectations. Statistical tests are done to evaluate reliability of the estimated parameter in accordance with statistical theory and expectation. The statistical test that were carried out included: 1 The t-test: this is used to test the significance of the individual parameters of the regression model. The decision to accept null hypothesis is based on the value of the test statistics from the data at hand. A high t – statistic indicates that the independent variable is significance in explaining the variation in the dependent variable. In this study, we will use a significance level of 0.05. 2 The f-test: this measure the overall significance of the model. The null hypothesis for the F-test is that all the regression coefficient is equal to zero, implying that the independent variables do not have any significance influence on the dependent variable. This was carried out to ascertain whether, an individual regression co-efficient is statistically significant. If the calculated F-statistic is greater than the critical value of the F-distribution, the null hypothesis is rejected, and it conclude that at least one independent variable has a significant influence on dependent variable. Conversely, if the calculated F-statistic is less than the critical value of the JAFM JAFM F-distribution, the null hypothesis is accepted, and it conclude that none of independent variable has a significant influence on dependent variable. 3 Co-efficient of Determination (R2): The goodness of fit test was carried out using the square of the correlation co-efficient. It shows or explains the percentage in total variation of the endogenous variable being explained by the change in the explanatory variables. It measures the extent to which the explanatory variables are responsive for judging the explanatory power of the regression. Second Order Econometric Tests The test was performed on the regression result in order to evaluate it according to the classical assumptions of Ordinal Least Square . These tests are discussed briefly below: 1. Test for multi-collinearity: This was used to test the linear collinearity among the explanatory variables and correlation matrix would be employed in this test. Multi-collinearity was used to test the linear collinearity among the explanatory variables, whether the independent variables are actually independent or they are linked to each. 2. Unit Root Test: the purpose of the test is to check the stationarity of the time series data in order to avoid having a spurious regression result. To check for stationarity, the Augmented Dickey-Fuller test was utilized. 3. Auto-correlation test: This was used to test if the errors corresponding to different observation are uncorrelated, testing for the randomness of error term. The Durbin-Watson (DW) method would be employed for this test, since according to Koutsoyannis (1997) D.W, provides estimates which have properties and are more efficient for all sample of all sizes. 4. Heteroscedasticity test: This was used to know whether error term of the explanatory variables of the estimated model have equal variance. 5. Normality test: This was used to know whether the error term of the estimated model is normally distributed. 6. Heteroscedasticity Test: Breusch-Pagan-Godfrey: Heteroscedasticity refers to situation where the variance of the residuals is unequal over a range of measured values. This is used to know whether error term of the explanatory variables of the estimated model have equal variance. 4.0 Result and Findings This chapter presents all the results of the empirical analysis with their interpretations. Data used for this analysis are time series that cover the period 2015-2025. The empirical results were generated using E-views 13 econometric software. Data Analysis The descriptive test summarizes the descriptive statistics of the model variable testing the appropriateness and normality of the variables as modeled. Summary result of the descriptive test is presented on Table 4.1 JAFM JAFM Table 4.1: Descriptive Statistics Source: Researchers’ Computation using E-views 13.0 (2026) Table 4.1 shows that, SRI, ROE, and ROA, had mean values of 1.038699, 21.77350, and 5.410957 respectively. This indicates the central or average values for these variables used in the study. In terms of the level of variability and dispersion in the distribution of these variables, the standard deviations obtained for the variables were 0.468159, 44.28072, and 8.200238 respectively. This indicates varying levels of variability in the distribution. Similarly, from the skewness values obtained, SR and ROE showed positive skewness values meaning they were all skewed to the right. This indicates that the mean values of these variables were greater than their median and mode. However, ROA showed negative skewness values meaning they were skewed to the left. The Kurtosis values obtained for the variables were showed in the table respectively. Since the values of the kurtosis are greater than three (3), it indicates a mesokurtic distribution, hence the presence of outliers in the data for the variables. Finally, based on the Jarque-Bera probability values obtained, all variables (SR, ROE, ROA,) indicated normality in their distribution, given that their Jarque-Bera probability was greater than 0.05. Multicollinearity Test In evaluating multicollinearity, the study examines the correlation coefficients using correlation matrix and the variance inflation factor . For correlation matrix, multicollinearity occurs when the magnitude of the correlation coefficient exceeds .80 (Kim, 2019; Benjamin, et al., 2023). Table 4.2: Correlation Matrix SR ROE ROA SR 1.000000 0.143936 0.138762 ROE 0.143936 1.000000 0.119408 ROA 0.138762 0.119408 1.000000 Source: Researcher’s Computation using E-views 13.0 (2026) From the correlation matrix result on Table 4.2, there is no problem of multicollinearity, given that the magnitude of the correlation coefficient does not exceed 0.80. SR ROE ROA Mean 1.038699 21.77350 5.410957 Median 1.016129 10.31192 5.130034 Maximum 3.273322 293.0710 26.49347 Minimum 0.195779 -88.00824 -30.09522 Std. Dev. 0.468159 44.28072 8.200238 Skewness 1.864935 30350339 -0.627622 Kurtosis 8.837431 18.72311 6.258269 Jarque-Bera 197.9486 1204.975 50.29181 Probability 0.000000 0.000000 0.000000 Sum 102.8312 2155.576 535.6848 Sum Sq. Dev. 21.47893 192156.7 6589.902 Observations 99 99 99 JAFM JAFM Table 4.3: Variance Inflation Factor Variance Inflation Factors Date: 06/17/26 Time: 03:32 Sample: 1 99 Included observations: 99 Coefficient Uncentered Centered Variable Variance VIF VIF ROA 0.000149 6.709341 4.659751 ROE 1.13E-06 1.283784 1.031772 C 0.007078 3.320662 NA Source: Researchers’ Computation using E-views 13.0 (2026) Heteroscedasticity refers to situation where the variance of the residuals is unequal over a range of measured values. This is used to know whether error term of the explanatory variables of the estimated model have equal variance. The result revealed there is no heteroscedasticity, given that the Prob (F-Statistic) and Prob (Chi-Square) are both 0. Table 4.4: Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 1.312286 Prob. F(6,92) 0.2596 Obs*R-squared 7.804835 Prob. Chi-Square(6) 0.2528 Scaled explained SS 24.95107 Prob. Chi-Square(6) 0.0003 SSource: Researchers’ Computation using E-views 13.0 (2026) Given that the probability value associated with the Chi-Squares is significantly greater than 0.05 levels, we accept the null hypothesis that there is no heteroscedasticity in the model. 4.2. Testing of Hypotheses HO: Return on Equity has no significance influence on stock returns of selected Manufacturing Companies in Nigeria. For Variance Inflation Factor , multicollinearity occurs when the VIF exceed 10. (Marcoulides and Raykov, 2019). The test conducted in Table 4.2 indicate the absence of violations of the assumption of multicollinearity on all the variables used in the study (given that Centred VIF values is less than 10). Heteroscedasticity Test: Breusch-Pagan-Godfrey JAFM JAFM Table 4.5: OLS Analysis showing the influence of ROE on stock returns of selected Manufacturing Companies in Nigeria Dependent Variable: SR Method: Least Squares Date: 06/17/26 Time: 05:46 Sample: 1 99 Included observations: 99 Variable Coefficient Std. Error t-Statistic Prob. C 1.005565 0.052205 19.26192 0.0000 ROE 0.001522 0.001062 1.432523 0.1552 R-squared 0.020718 Mean dependent var 1.038699 Adjusted R-squared 0.010622 S.D. dependent var 0.468159 S.E. of regression 0.465666 Akaike info criterion 1.329298 Sum squared resid 21.03393 Schwarz criterion 1.381725 Log likelihood -63.80027 Hannan-Quinn criter. 1.350510 F-statistic 2.052122 Durbin-Watson stat 2.024433 Prob(F-statistic) 0.155209 Source: Researchers’ Computation using E-views 13.0 (2026) The regression line can be written as follows: SR = 1.005565 + 0.001522 ROE + e The equation implies that if the independent variable were held constant, SR will grow on an average rate of 1.005565. Furthermore, the results indicated that ROE exhibited positive relationship with the SR with a coefficient of 0.001522. This means that if other factors remain unchanged, a one-unit increase in ROE will lead to a one-unit increase in SR by 0.001522%. The statistical significance of the above relationships was given by the p-value associated with each of the variables. Since this study t-test is based on the 95% level of confidence, a variable is said to have significant effect if its p-value is less than or equal to 0.05. Therefore, with the p-value of 0.1552, ROE is said to have an insignificant effect on SR, given that the p- values is greater 0.05. The R-squared value of 0.020718 indicates that about 0.020718 variation in SR is accounted for by the independent variable of this study. In order to test the hypothesis, the researcher relied on the p-value of the F-statistic. The result shows the p-value of 0.1552 which implies that the independent variable has an insignificant effect on the dependent variable. Therefore, the null hypothesis which states that there is no significant influence of ROE on stock returns of selected Manufacturing Companies in Nigeria, is accept. HO2: Return on Assets has no significance influence on stock returns of selected Manufacturing Companies in Nigeria. JAFM JAFM Table 4.6: OLS Analysis showing the influence of ROA on stock returns of selected Manufacturing Companies in Nigeria. Dependent Variable: SR Method: Least Squares Date: 06/17/26 Time: 05:55 Sample: 1 99 Included observations: 99 Variable Coefficient Std. Error t-Statistic Prob. C 0.995834 0.056200 17.71933 0.0000 ROA 0.007922 0.005741 1.379993 0.1708 R-squared 0.019255 Mean dependent var 1.038699 Adjusted R-squared 0.009144 S.D. dependent var 0.468159 S.E. of regression 0.466014 Akaike info criterion 1.330791 Sum squared resid 21.06535 Schwarz criterion 1.383218 Log likelihood -63.87415 Hannan-Quinn criter. 1.352003 F-statistic 1.904382 Durbin-Watson stat 2.034956 Prob(F-statistic) 0.170761 Source: Researchers’ Computation using E-views 13.0 (2026) The regression line can be written as follows: SR = 0.995834+ 0.007922 ROA + e The equation implies that if the independent variable were held constant, SR will grow on an average rate of 0.995834. Furthermore, the results indicated that ROA exhibited positive relationship with the SR with a coefficient of 0.007922. This means that if other factors remain unchanged, a one-unit increase in ROA will lead to a one-unit increase in SR by 0.007922. The statistical significance of the above relationships was given by the p-value associated with each of the variables. Since this study t-test is based on the 95% level of confidence, a variable is said to have significant effect if its p-value is less than or equal to 0.05. Therefore, with the p-value of 0.1708, ROA is said to have an insignificant effect on SR, given that the p- values is greater 0.05. The R-squared value of 0.020718 indicates that about 0.019255 variation in SR is accounted for by the independent variable of this study. In order to test the hypothesis, the researcher relied on the p-value of the F-statistic. The result shows the p-value of 0.1708 which implies that the independent variable has an insignificant effect on the dependent variable. Therefore, the null hypothesis which states that there is no significant influence of ROA on stock returns of selected Manufacturing Companies in Nigeria, is accept. 4.3 Discussion of Findings The result of the first hypothesis indicates that about 0.020718 % variation in SR is accounted for by the independent variable of this study. In order to test the hypothesis, the researcher relied on the p-value of the F-statistic. The result shows the p-value of 0.020718 which implies that the independent variable has an insignificant effect on the dependent variable. Therefore, the null hypothesis which states that there is no significant influence of ROE on stock returns of selected Manufacturing Companies in Nigeria, is accept. The finding is in contrast with the works of Handito and Wiwiek, (2019); Fitri (2023). JAFM JAFM The result of the second hypothesis indicates that about 0.019255 variation in SR is accounted for by the independent variable of this study. In order to test the hypothesis, the researcher relied on the p-value of the F-statistic. The result shows the p-value of 0.1708 which implies that the independent variable has an insignificant effect on the dependent variable. Therefore, the null hypothesis which states that there is no significant influence of ROA on stock returns of selected Manufacturing Companies in Nigeria, is accept. The result of the findings is in contrast with prior studies of Dewa and Anak (2021); Fitri (2023) and Angguliyah and Roy (2022). 5.0 Summary and Conclusion 5.1 Summary of the major findings The followings are the summary of the findings: i. Return on equity has a positive and insignificant effect on stock returns of selected Manufacturing Companies in Nigeria ii. Return on assets has a positive and insignificant effect on stock returns of selected Manufacturing Companies in Nigeria 5.2 Conclusion The predict variables has weak statistically influence on stock returns of selected manufacturing companies in Nigeria. This implies investment portfolio holders do not only rely on these indicators when investing in the sector. Business Implications of the Findings In line with the recommendations of the study, the following business implications were made: i. The insignificant effect of accounting income on stock returns suggests that investors in the Nigerian manufacturing sector may prioritize other factors, such as market trends and macroeconomic conditions, over financial statements. This implies that businesses must complement their financial disclosures with other value-driven strategies to attract investors. ii. Companies should focus on improving financial reporting quality and transparency to enhance investor confidence. The findings indicate that financial statements alone may not be enough to drive stock prices, possibly due to concerns about earnings management or information asymmetry. Strengthening corporate governance and adopting high-quality financial reporting standards can improve the credibility of accounting information. 5.4 Recommendations i. Drawing from the finding that ROE has a positive and insignificant influence on SR, Manufacturing companies in Nigeria should optimize their equity utilization by ensuring that they have an optimal mix of debt and equity financing. iii. Considering the finding that ROA has a positive and insignificant influence on SR, Manufacturing companies should prioritize asset utilization and efficiency to improve their ROA. This can be achieved by optimizing asset utilization, reducing asset redundancy, and improving asset management practices. 5.5 Suggestions for further studies Further studies should analyze how macroeconomic variables (e.g., inflation, exchange rates, interest rates, and GDP growth) and industry-specific factors (e.g., competition, government policies, and production costs) affect the stock returns of manufacturing firms. JAFM JAFM 5.6 Contribution to Knowledge This study contributes to the body of knowledge in accounting and finance by providing empirical evidence on the relationship between accounting income and stock returns in the Nigerian manufacturing sector. Unlike many prior studies that establish a strong link between financial performance and stock market behavior, this research reveals a statistically insignificant effect, suggesting that investors may rely more on non-accounting factors when making investment decisions. Additionally, the study sheds light on the potential inefficiencies in the Nigerian stock market, where accounting information may not be fully reflected in stock prices. This finding contributes to the discourse on market efficiency in developing economies and calls for further investigation into alternative determinants of stock returns. JAFM JAFM References Angguliyah, K., and Roy, K., (2022). The effect of Return on Assets and Earning Per Share On Stock Returns with Exchange Rates as moderating variables. International Journal of Business Marketing and Management , 7(3), 53-63 Benjamin, O. O, Akinola, G., W and Adeoba, A. A., (2023). Fossil energy consumption carbon dioxide emission and adult mortality rate in Nigeria. Managing Global Transitions, 21(4), 353 – 384 Brigham, Y. and Houston, T. (2007). Analysis of the effect of accounting earnings and operating cash flow to stock return manufacturing companies listed on stock exchanges in Indonesia. Students' Journal of Accounting and Banking, 1(1), 74-83. Dewa, A. and Anak, A. (2021). The Effect of Profitability on Stock Return. American Journal of Humanities and Social Sciences Research , 5 (1), 695-70 Dyna, S, and Jason, R.H, (2022). “Fundamental Analysis and the Prediction of Earnings”. International Journal of Business and Management, 7, 32-41. Fitri, M., (2023). Effect of return on assets and return on equity to the stock price on PT. Indocement Tunggal Prakarsa Tbk. Journal of Financial Economics, 121(1), 28-45. Fuhrmann, U. (2017). Financial performance, exchange rate and stock return: Evidence from manufacturing sector. Journal of Technology Management, 18(3), 205-217 Gilbert, O. and Ugochukwu, J. N., (2023). Drivers of Operating Profit: A Focus on Selected Firms’ Costs. CECCAR Business Review, 1(8), 44 – 59. Gujarati, D. (2024). Basic-econometrics (5 ed.). New York: Douglas Reiner, 46 – 49. Gurung, R. and Subedi, P. (2024). Assessing the Effect of Earnings per Share on Equity Prices at Commercial Banks in Nepal. Interdisciplinary Journal of Management and Social Sciences , 5(2), 177-187 Gunanta, I., Sherlita, F.and Lestari, S. (2020). The effect of the cash flow statement and Earning per Share on stock prices. International Conference on Accounting Studies , 17-2 Hanafi, M., and Halim, O., (2016). On the usefulness of earnings and earnings research: Lessons and directions from two decades of empirical research. Journal of Accounting Research, 5(2), 153–192. Handito, J., and Wiwiek, M., (2019). The influence of Earning Per Share , Price Book Value , Return On Asset , Return On Equity . American Economic Review, 76(2), 323-329. Jensen, E, (1986). Comparative Analysis of Stock Price Behaviour on the Bombay, London, New York Stock Exchanges. Journal of Financial and Quantitative Analysis, 1(7), 391- 413 Kasmir, A. (2016). Financial Leverage, Firm Size and Profitability of Quoted Insurance Companies in Nigeria. World Economics & Finance Bulletin, 3(1), 67 – 79 Kim, J. H (2019). Multicollinearity and misleading statistical results. Korean Journal of Anesthesiology, 72(6); 558. Leo, H., and Harlyn, S. (2022). The Effect of Gross Profit, Operating Profit and Net Profit on Future Cash Flow Prediction at the Company of Telecommunications Sub Sector on IDX in 2014-2019. Journal of Economics and Business, 6(1), 348-35 Maham, I., Ball, R., and Brown, P. (2008). An empirical evaluation of accounting income numbers. Journal of Accounting Research, (2)3, 159–178. Marcoulides, K. M., and Raykov, T., (2019). Evaluation of variance inflation factor in regression model using latent variable modeling methods. Educational and Psychological Measurement, 79(5), 874 – 882. JAFM JAFM Meyer U. (1984). Commerce – Its Theory and Practice: Commerce – Its Theory and Practice: Great Britain, Chaucer press Ltd., 483p. Okoye, R. (2020). The effect of operating cash flow on earnings management of Nigerian Banks. Journal of Finance Research, 1(8), 223-237. Ojimba, F.,Okegbe, T. O., Ifurueze, M. S. (2021). 'Cash Flow Activities and Stock Returns: Evidence from Nigerian Consumer Goods Firms'. International Journal of Trend in Scientific Research and Development , 5(6),1259- 1266, Rezky, R., Abdul, T. and Damayanti, R. (2022). Analysis the effect of Accounting Information on Stock Returns Using the Error Correction Model . Journal of Research in Business and Management, 10(10), 72-76 Saeedi, M. and Ebrahimi, I., (2020). The effect of ROA, ROE, NPM and CR on stock returns in companies listed on the Jakarta Islamic Index . I-Finance: A Research Journal on Islamic Finance, 2(2), 54-71. Sakr, T., (2022). The influence of good corporate governance practice on the stock price (Study on Company of LQ45 Index in Indonesia stock exchange during 2012-2016). Business Administration, 50 (6), 164-173. Saragih, J. L. (2018). The Effects of Return on Assets ( ROA ), Return on Equity ( ROE ), and Debt to Equity Ratio ( DER ) on Stock Returns in Wholesale and Retail Trade Companies Listed in Indonesia Stock Exchange. International Journal of Sciene and Research Methodology, 8(3), 348–367. Tandelilin, E. (2020). Factors Affecting The Return Stock Company In Indonesia Stock Exchange LQ45 In Years 2012-2015.Economic, Business, Management, and Information System Journal, 17(1),37- 45. Zutter, C. J. and Scott, B. S., (2019) Principles of Managerial Finance, 15th Edition. United States: Pearson Education Limited, 749p.